Unravelling the many facets of human cooperation in an experimental study

Humans readily cooperate, even with strangers and without prospects of reciprocation. Despite thousands of studies, this finding is not well understood. Most studies focussed on a single aspect of cooperation and were conducted under anonymous conditions. However, cooperation is a multi-faceted phenomenon, involving generosity, readiness to share, fairness, trust, trustworthiness, and willingness to take cooperative risks. Here, we report findings of an experiment where subjects had to make decisions in ten situations representing different aspects of cooperation, both under anonymous and ‘personalised’ conditions. In an anonymous setting, we found considerable individual variation in each decision situation, while individuals were consistent both within and across situations. Prosocial tendencies such as generosity, trust, and trustworthiness were positively correlated, constituting a ‘cooperativeness syndrome’, but the tendency to punish non-cooperative individuals is not part of this syndrome. In a personalised setting, information on the appearance of the interaction partner systematically affected cooperation-related behaviour. Subjects were more cooperative toward interaction partners whose facial photographs were judged ‘generous’, ‘trustworthy’, ‘not greedy’, ‘happy’, ‘attractive’, and ‘not angry’ by a separate panel. However, individuals eliciting more cooperation were not more cooperative themselves in our experiment. Our study shows that a multi-faceted approach can reveal general behavioural tendencies underlying cooperation, but it also uncovers new puzzling features of human cooperation.


Supplementary Figure
. Effect of the subjects' facial appearance on the shift in behaviour of their interaction partners.
Association between the subjects' facial appearance (columns) and the behavioural shifts elicited in their interaction partners in five decision situations (rows).The heat map gives a pictorial representation of the R 2 values for positive and negative associations, based on linear regression models with a single predictor.Significant associations: *p<0.05,**p<0.01.
Facial appearance is quantified by the face judgement scores regarding eight attributes (attractive, happy, generous, trustworthy, rational, risk-taking, greedy, angry) and the scores for their principal components (PC1, PC2).For each subject, the shift in their partners' behaviour was calculated as follows.Each partner received a score of +1, when the partner's decision in the interaction with the given subject changed in the positive direction when compared to the anonymous setting, a score of -1 if it changed in the negative direction, and a score of 0 if it did not change.These scores were subsequently averaged over all partners who interacted with the given subject in a personalised setting.Finally, the mean scores were regressed upon the subjects' face judgement scores per attribute.
The five binary decision situations had to be left out of the analysis, as in these games the scores for a shift in behaviour are intrinsically biased: if the decision was a '0' in the anonymous setting, it could only change in the upward direction in the personalised setting; if it was a '1', it could only change in the downward direction.S3.Relationship between facial attributes and prosocial behaviour: Effect of pooling anonymous and personalised decisions on the test results in Figure 4c.

Supplementary Figure
From Fig. 4 in the main text, we concluded that the results of our experiment do not provide evidence for the hypothesis that facial attributes of an individual are associated with the individual's propensity for prosocial behaviours.The analysis in Fig. 4c was solely based on the decisions made under anonymous conditions.As the decisions under anonymous conditions were, to a certain extent, associated with the decisions under personalised conditions (see Supplementary Table S3), the power of the tests underlying Fig. 4c might be enhanced by pooling the three decisions made by each individual (one under anonymous and two under personalised conditions).The heat map above illustrates that pooling the three decisions in a given cooperation context does not produce clearer associations between facial attributes and prosocial decisions than the analysis in Fig. 4c.
Heat map of the p-values of 100 tests addressing the statistical association between each of ten facial scores (columns: the eight facial attributes and the scores for PC1 and PC2) and the pooled decisions (anonymous and personalised) in the ten experimental situations (rows).As in Fig. 4c, we conclude that the number of significant pvalues (one for p<0.05; six for p<0.10) does not exceed the expected number of type II errors.
Generalized Estimating Equations accounting for repeated measurements were used: linear models, where scores for facial judgements were set as response variables, and decisions in the experimental situations were set as independent factors (for binary scaled decisions) or covariates (for continuously scaled decisions).S1.Correlation matrix underlying the heatmap in Figure 2a: Associations between decisions in different experimental situations in the anonymous setting.Supplementary Table S2.Economic games used in the study of Peysakhovich et al. (2014;ref 13).

Generosity
Dictator Game (DG) One-sided decision to donate 0 ≤ x ≤ 50 points to the interaction partner, keeping 100-x points for themselves.

Trust
Trust Game (TG1) The trustor decides whether to entrust 50 points to the trustee.If entrusted, the number of points is tripled, and the trustee decides how many points (up to 150) to return to the trustor.Unreturned points are kept by the trustee.

Trustworthiness
Trust Game (TG2) Player 1 makes a proposal on how to allocate 100 points between self and Player 2. [This part of the game was not analysed in ref. 13.] Demand (MAO) Ultimatum Game (UG2) Player 2 indicates their "minimal acceptable offer (MAO)".Any offer of Player 1 below the MAO will be rejected (and both players receive nothing).
Free-riding Public Goods Game (PGG) Four participants start with a 100-point endowment each.Each player decides how many points (from 0 to 100) to contribute to a common project.The individual contributions are lost for the players.However, the sum of all four contributions is doubled and distributed evenly among the four players.

Risk-taking (competitiveness)
All-Pay Auction (AP) Two players start with a 100-point endowment each.Each player decides how many points (from 0 to 100) to invest in the competition for a 100-point prize.The money invested is lost, but the player who invests more receives the 100 points (in case of a draw, the winner is assigned randomly).

Punishment
Second Party Punishment (2PP) Two players start with a 100-point endowment each.In a first stage, each player decides whether to give up 30 points to increase the other player's endowment by 60 points (C) or not (D).In a second stage, each player can pay 0 ≤ y ≤ 14 points to reduce the other player's payoff by 5•y points.The values of y can be made dependent on whether the other player chose C or D in stage 1.

Third-party punishment
Third-Party Punishment (3PP) Two players are each endowed with 100 points.Player A can decide whether to 'take' from Player B. If A takes, then B loses 50 points while A gains 30 points.A third player, C, receives 20 points and can pay 0 ≤ y ≤ 20 points to reduce A's payoff by 5•y points if A decided to 'take' from B.
For comparative purposes, we use only the results of Experiment 1 from ref 13.This part involved 576 participants recruited using Amazon's Mechanical Turk, with no sex or nationality of the participants specified.Participants earned 50 cents for accepting the online survey.They then made decisions in the above-listed economic games.After completing the study, one role in one of these games was chosen at random, participants were matched accordingly and then paid based on their earnings in that game (up to $2, depending on which game was picked and what the players' decisions were).